1. Field of the Invention
The present invention relates generally to the field of neural response and more particularly to techniques for analyzing measured neural response data.
2. Description of the Related Art
Research has been done in the past to collect neural response data from individuals presented with two choices. The neural response data is typically collected from the brain using electroencephalogram (“EEG”) or functional magnetic resonance imaging (“fMRI”) equipment that can generate tens or hundreds of thousands of data points for a test subject. The neural response data has been analyzed to attempt to draw a correlation between the neural response data and the two choices. The goal of this analysis is to determine which of the two choices the test subject is going to select by examining the neural response data. The correlation of the neural response data with one of the two choices is referred to as classification. For example, if the test subject is shown an apple and an orange, the neural response data is collected and classified to determine whether the test subject is going to choose the apple or the orange. Because the test subject from whom the neural response data is collected only has two choices, the mathematical analysis is referred to as binary classifier analysis or binary discrimination analysis. A mathematical model for binary discrimination is described in an article by S. Perkins, K. Lacker, and J. Theiler, titled “Grafting: Fast, incremental feature selection by gradient descent in function space,” published in the Journal of Machine Learning Research, 3:1333-1356 (2003), which article is incorporated herein by reference.
When a test subject is presented with more than two choices, such as selecting a key on a keyboard, the neural response data and the analysis thereof is more complicated. Each collected data point is a vector in a high dimensional feature space. Most of the features associated with each data point are irrelevant to the separation of the data into classes and including the irrelevant data decreases the performance of the classifier. Techniques other than the one described in the Perkins article have been used to study the discrimination between more than two mental states, in other words, when the test subject has more than two choices. However, prior techniques for multiclass discrimination have had limited success due to the complexity of the neural response data.
Previous work has been done in the classification of data into multiple categories, such as can be found in J. Zhu and T. Hastie, Kernel Logistic Regression and the Import Vector Machine, in T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, NIPS, pages 1081-1088, MIT Press, 2001. However, this previous work has primarily focused on the case where the desired set of features is known ahead of time, and not on the case where a large number of features are irrelevant. Irrelevant features seriously degrade the performance of these types of classifiers, including support vector machines. What is needed is an approach to creating classifiers where the optimal feature kernel is not known ahead of time.
Research has been performed on distinguishing more than two mental states in fMRI data. Most of this research has focused on using basic machine learning techniques, but the performance of these techniques has been limited, particularly where there are a large number of features associated with the data.
For example, the Pittsburgh Brain Activity Interpretation Competition 2007 (PBAIC 2007) was an academic competition to determine techniques to classify fMRI data into multiple brain states. The results of the competition can be found at http://pbc.lrdc.pitt.edu/?q=2007-results. A variety of techniques were used in this competition, however, none provided a solution that unified feature selection and weight determination.
Previous efforts into classifying neural response data using EEG data have concentrated on the binary problem. Research on distinguishing more than two mental states in EEG data has been more limited. Because of the difficulty of distinguishing states, little has been done when the number of classes is larger than two.